Our framework estimates risk exposure using tools from the finance literature. We resort to two well established statistical techniques used to measure the level of financial risk within a firm or an investment portfolio over a specific time frame; the Value-at-Risk (VaR) and the Conditional-Value-at-Risk (CVaR). VaR answers the question of what is the maximum loss with a specified confidence level [1]}. CVaR is the expected loss if that confidence level is ever crossed. Though VaR is a widely accepted and used measure, it is not coherent as it violates the subadditivity property of coherent measures [2]}. VaR is neither convex nor smooth, and it may result to multiple local extrema [3]}. As a result, optimizing this measure under different constraints is quite complex. For this reason, we focus on CVaR, which is a coherent and convex risk measure and can be optimized using linear optimization [4]}, [2]}.
